白昼红外星图星点提取方法
doi: 10.11728/cjss2024.05.2023-0123 cstr: 32142.14.cjss2024.05.2023-0123
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杨原 男, 1994年11月出生于河南省渑池县, 现为信息工程大学在读研究生, 主要研究方向为天文导航、空间目标检测等. E-mail: yangyuan00001@163.com -
李崇辉 男, 1987年2月出生于四川省资阳市, 现为信息工程大学地理空间信息学院副教授, 主要研究方向为自主导航与组合导航、天文导航等. E-mail: 13525504183@126.com
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Method of Star Point Extraction for Daytime Infrared Star Image
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摘要: 白昼红外星图具有高背景噪声、低信噪比、弱目标的特征, 采用传统单帧提取方法很难准确提取星点. 且由于星点为弱小目标, 对噪声极为敏感, 星点成像多为随机不规则图形, 采用单帧提取星点质心鲁棒性较差. 传统多帧叠加法虽能克服单帧提取星点质心鲁棒性较差的问题, 但对于高背景噪声红外星图, 叠加星图并不能明显提高信噪比, 星点提取成功率依然较低. 为此, 提出利用背景预测方法确定疑似星点位置, 并对边界进行膨胀, 然后利用膨胀后的边界进行单帧星图能量提取, 并将提取星图进行叠加形成高信噪比星图, 最后进行星点质心提取. 实验表明, 该方法星点提取正确率为99.5%, 较自适应阈值分割法和多帧叠加法分别提升84.2%和37.9%, 较背景预测法正确率提升14.5%. 同时该方法较自适应阈值分割法和背景预测法、多帧叠加法精度分别提升12.8%, 41.4%, 33.3%, 具有明显优势.Abstract: The daytime infrared star image has the characteristics of high background noise, low signal-to-noise ratio and weak target, so it is difficult to accurately extract the star centroid by using the traditional single frame extraction method. Because the stars are weak and small targets, they are extremely sensitive to noise, and the star imaging is mostly random and irregular, so the robustness of extracting the star centroid by single frame is poor. Although the traditional multi-frame superposition method can overcome the problem of poor robustness of star centroid extraction in single frame, for infrared star images with high background noise, the superposition star image cannot significantly improve the signal-to-noise ratio, and the success rate of star extraction is still low. Therefore, this paper proposes a method that uses background prediction method to determine the position of suspected stars, expands the boundary, and then uses the expanded boundary to extract the energy of single frame star image, and superimposes the extracted energy star image to form a high signal-to-noise ratio star image, and finally extracts the star centroid. Experiments show that the accuracy of star extraction is 99.5%, which is 84.2% and 37.9% higher than that of adaptive threshold segmentation method and multi-frame superposition method, respectively, and 14.5% higher than that of background prediction method. At the same time, the accuracy of this method is improved by 12.8%, 41.4% and 33.3%, respectively, compared with the adaptive threshold segmentation method, background prediction method and multi-frame superposition method, which has obvious advantages over the traditional method.
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图 2 连续4帧红外星图星点成像形状
Figure 2. Imaging shape of star points in infrared star images for four consecutive frames
图 3 星图处理对星点能量分布影响
Figure 3. Influence of star image processing on star point energy distribution
图 4 多帧叠加信噪比效果
Figure 4. Effect of multi-frame superimposed Signal-to-Noise Ratio (SNR)
图 5 基于背景预测的多帧叠加法流程
Figure 5. Multi-frame stacking method based on background prediction
图 7 疑似星点边界膨胀后能量提取
Figure 7. Energy extraction after expansion of suspected star boundary
图 10 单帧提取目标星点位置散点
Figure 10. Scatter plot of target star point position extracted in a single frame
图 11 多帧提取目标星点位置散点
Figure 11. Scatter plot of target star point position extracted from multiple frames
表 1 单帧提取星点质心对比
Table 1. Comparison of star centroids extracted from single frame
单帧提取方法 x 坐标均值/pixel y 坐标均值/pixel 距离误差均值/pixel 均方根误差/pixel 提取成功帧数 成功率/(%) 自适应阈值分割法 330.01 349.99 0.39 0.21 92 15.3 背景预测法 329.99 350.03 0.46 0.28 486 81.0 
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表 2 各帧提取星点统计
Table 2. Statistics of extracted star points for each frame
方法 自适应阈值分割法 背景预测法 提取星点数 0 1 2～3 4～7 ≥8 0 1 2 3 帧数 82 92 182 119 117 25 486 78 11
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表 3 多帧叠加法提取星点质心对比
Table 3. Comparison of star centroids extracted by multi-frame superposition method
多帧叠加提取法 x 坐标均值
/pixely 坐标均值
/pixel信噪比均值 距离误差均值
/pixel均方根误差
/pixel提取成功帧数 成功率/(%) 传统多帧叠加法 330.01 350.01 0.6379 0.18 0.12 346 57.6 本文所提方法 330.00 350.01 1.1322 0.12 0.07 597 99.5
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表 4 多帧叠加提取质星点统计
Table 4. Statistics of multi-frame superposition extraction of mass star points
方法 传统单帧星点提取法 本文所提方法 提取星点数 0 1 2 3 4～8 ≥8 0 1 帧数 70 346 37 23 82 42 3 597
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